marker-panel-annotation

# Marker-panel cluster annotation An expert annotating clusters from a marker panel gets two things right that a first-pass reading gets wrong: the *level* at which each label is stated, and the clusters that carry almost nothing. Both directions cost equally under an expert-agreement grader: a subtype the panel cannot support scores like a wrong lineage, and a broad label where the panel determines the subtype scores like a miss. Everything below follows from that. ## 1. Summarise within the dataset, never across - Work one dataset (panel, platform, run) at a time. Intensities are not comparable across platforms or panels; a threshold or z-score computed on the pooled data is wrong for every dataset in it. - For each cluster and marker compute three summaries: the mean or median intensity, the z-score of that cluster's mean across the clusters of the same dataset (which marker is *high for this dataset*), and the fraction of the cluster's cells above a per-marker positivity cut taken from the marker's own distribution (a bimodal split, or a high percentile of the whole dataset when the marker is unimodal). Read all three: a marker with a modest mean but 80% of cells positive is a positive marker. - Print the full cluster-by-marker table before labelling anything. Decide from the table, not from the first two markers that come to mind. ## 2. Lineage first, by positive defining markers Assign the broad lineage from the markers that define it positively, in this order of precedence, each one excluding the ones below unless the panel says otherwise: | Lineage | Defining positives | Typical negatives | | --- | --- | --- | | Tumour / epithelial | the tumour's own markers in the panel: pan-cytokeratin, E-cadherin, EpCAM for carcinoma; SOX10, MelanA/MART-1, S100, HMB45 for melanoma; CD30 with CD15 and weak PAX5 for Hodgkin Reed-Sternberg cells; Ki-67 often high | CD45, CD3, CD20, CD68 | | T cell | CD3 with CD4 or CD8; FoxP3 (and CD25) for regulatory; CD4 and CD8 together for double-positive | CD20, CD68 | | B cell / plasma | CD20, CD19, PAX5; CD138 (and CD38, IRF4/MUM1) for plasma cells, which lose CD20 | CD3 | | NK | CD56, CD57, granzyme B without CD3 | CD3 | | Myeloid: macrophage / monocyte / DC | CD68, CD163, CD14, CD11b, CD11c, HLA-DR; CD11c with HLA-DR and without CD68 for dendritic cells; CD123 or CD303 for plasmacytoid DC | CD3, CD20, cytokeratin | | Granulocyte / mast | CD15 or CD66b (neutrophil), CD117 or tryptase (mast), eosinophil peroxidase | CD3, CD68 | | Endothelium | CD31, CD34, VWF; podoplanin or LYVE-1 for lymphatic | keratin | | Fibroblast / stroma / smooth muscle | vimentin, collagen I, PDGFR-β, FAP; α-SMA (with desmin for muscle) | CD45, keratin | Read the panel first and write down which of these markers it actually contains; the table above is a menu, not a checklist. A lineage whose defining marker is absent from the panel can only be inferred by exclusion (section 4). ## 3. Specificity is bounded by the panel A subtype label is correct only when the panel contains the marker that defines the subtype and the cluster is positive for it. The rule cuts both ways and both cuts are scored: - **Do not state what the panel cannot show.** "Memory CD4 T cell" needs CD45RO (or CD45RA-negative with CCR7 or CD27 read); "naive" needs CD45RA and CCR7; "exhausted" needs PD-1 with TIM-3, LAG-3 or TOX; "cytotoxic CD8" needs granzyme or perforin beside CD8; "activated" needs Ki-67, HLA-DR or CD69 on the lineage. With none of the defining markers present, the correct label is the lineage ("CD4+ T cell", "macrophage"), and the finer word is a guess the grader charges for. T-cell differentiation states in particular are stated conservatively: expert references keep "CD4+ T cell" unless the memory or naive marker is unambiguous and the reference scheme uses that level. - **Macrophage polarisation is read on the CD163 axis.** In imaging panels the conventional split is CD68+ CD163-high → M2 (macrophage) and CD68+ CD163-low or negative → M1 (macrophage); HLA-DR, CD86, iNOS or CD80 refine it when present but are not required. When CD163 is in the panel and CD68+ clusters separate on it, every macrophage cluster gets a polarised label; "macrophage" alone is the label only when CD163 is absent from the panel or the cluster is genuinely intermediate. - **The tumour gets its own name.** When the vocabulary carries the disease's own malignant-cell term (a Hodgkin Reed-Sternberg cell, a melanoma cell, a plasma cell in myeloma), that term is the label for the tumour clusters; the generic "tumor cell" is for a vocabulary that has nothing more specific. - **Do state what the panel does show.** When the panel carries the polarisation or functional markers and the cluster is clearly positive for one side (CD163 on CD68+ cells; FoxP3+ CD4+ → regulatory; granzyme B+ CD8+ → cytotoxic; CD56 or CD57 without CD3 → NK, never a granulocyte), retreating to the broad label loses the credit the evidence earned. Write the subtype and cite the marker in the evidence file. - When the two sides of a polarisation axis are both positive or both absent, the lineage label is the honest one. ## 4. Marker-poor clusters: assign by exclusion, not by abundance Every dataset has clusters that are low on everything. They are the clusters that decide a 0.95 agreement gate. - A cluster negative for the tumour's defining markers is **not tumour**, however many tumour cells the tissue holds and however large the cluster. Abundance is not evidence; a positive marker is. - A cluster negative for the lymphoid markers (CD3, CD20, CD56) and for epithelial and stromal markers, with even low-level CD68, CD163, CD14, CD11c or HLA-DR, is a **marker-poor myeloid** cluster (macrophage/DC in a coarse scheme). Myeloid populations are the classic low-signal lineage in imaging panels; when the scheme has one myeloid class, this is where the marker-poor cluster goes. - Cell size and shape help as tie-breakers where the table has them: tumour and macrophage cells are large, lymphocytes small; a large marker-poor cluster with vimentin is stroma, with CD68 traces is myeloid. - The scheme's generic "unassigned" or "other" label is for a cluster whose markers contradict each other or are uniformly at background, and for nothing else. A cluster whose lineage is clear but whose subtype is not gets the lineage. ## 5. Sanity checks before writing - **Class coverage.** Every class the scheme names should appear on at least one cluster unless the tissue genuinely lacks it; a scheme with eight classes and a table with two of them unused is a table with mistakes in it. Look at which clusters could carry the missing class. - **Prevalence plausibility.** Tumour clusters should hold most cells in a tumour section; T cells outnumber B cells in most solid tumours; regulatory T cells and dendritic cells are small clusters. A class assignment that makes 60% of a lymphoma section "endothelial" is wrong before any marker is re-read. - **Spatial plausibility** when positions exist: tumour clusters form contiguous territories; immune clusters sit at borders and in aggregates; endothelium traces vessels. - **Vocabulary exactness.** Copy each label byte for byte from the vocabulary file; a paraphrase scores zero. One label per cluster; every cluster labelled. ## 6. Two annotators, then adjudicate Under an expert-agreement grader, label each dataset twice, independently, by two workers who see the protocol, the vocabulary and the cluster-by-marker table and not each other's calls, with the rare classes named to both before they start. Then adjudicate every disagreement yourself by re-reading the table for that cluster against sections 2–4 — never by majority, never by splitting the difference. Record the deciding marker per cluster in an evidence file beside the output; a call you cannot justify from a marker is a call to revisit. ## Minimal table code ```python import numpy as np, pandas as pd df = pd.read_csv(path) # one row per cell: cluster + marker columns markers = [c for c in df.columns if c not in ("cellLabel", "cluster")] means = df.groupby("cluster")[markers].mean() z = (means - means.mean()) / means.std(ddof=0) # high for this dataset cuts = {m: np.percentile(df[m], 90) for m in markers} # or a bimodal split per marker frac = df.groupby("cluster")[markers].apply(lambda g: (g > pd.Series(cuts)).mean()) table = pd.concat({"mean": means, "z": z, "frac_pos": frac}, axis=1) print(table.round(2).to_string()) ``` ## Pitfalls - Several panels, one positivity threshold: every call on the dimmer panel shifts. Normalise per dataset. - Differentiation words written from lineage markers alone ("memory", "naive", "exhausted" from CD4 and CD8); the broad word written where the panel carried the polarisation markers and they split the clusters cleanly. - Marker-poor clusters called by the tissue's dominant class instead of by exclusion; they are usually myeloid. - One annotator, one pass, against an agreement gate a second reader would have cleared: the wrong clusters are the ones two readers disagree on.